Therefore, this work aims to tackle the challenges in achieving scalability and generalization of the data-driven HyChem model using techniques developed for training deep neural network models. We propose the stochastic gradient descent (SGD) optimizer to learn the pyrolysis submodel in HyChem models. The SGD optimizer is widely applied in the deep learning communities for its efficiency as well as scalability in dealing with large datasets, and its robustness in optimizing high-dimensional non-convex neural network models. Our recent work [1]} has also shown that a chemical reaction network is equivalent to a neural network with a single hidden layer. Similarly, solving ordinary differential equations (ODEs) of reaction network models is equivalent to solving infinite-depth deep residual networks [2]}, which further rationalizes exploiting SGD in training HyChem models. However, the base model is constrained using the sequential optimization from multiple datasets, and thus changing the kinetic parameters too much has the risk of over-fitting the experimental dataset. Therefore, we also adopt various regularization techniques used in training deep neural network models to increase the generalization performance of learned models. Thus, we can achieve a model that has comparable performance to the conventional GA approach, but with much smaller changes in the kinetic parameters from the reference values.
